NVIDIA Open Source

NVIDIA cuDNN

Deep Learning Software

Deep learning algorithms use large amounts of data and the computational power of the GPU to learn information directly from data such as images, signals, and text. Deep learning frameworks offer flexibility with designing and training custom deep neural networks and provide interfaces to common programming language. For developers the NVIDIA Deep Learning SDK offers powerful tools and libraries for the development of deep learning frameworks such as Caffe2, Cognitive toolkit, MXNet, PyTorch, TensorFlow and others.

Deep Learning Frameworks

Deep learning frameworks offer building blocks for designing, training and validating deep neural networks, through a high level programming interface. Widely used deep learning frameworks such as Caffe2, Cognitive toolkit, MXNet, PyTorch, TensorFlow and others rely on GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training.

Optical Flow for Video Inference (Optical Flow SDK): Set of high-level APIs that expose the latest hardware capability of Turing GPUs dedicated for computing the optical flow of pixels between images. Also useful for calculating stereo disparity and depth estimation.

High level SDK for tuning domain specific DNNs (Transfer Learning Toolkit): Enabling end to end Deep Learning workflows for industries

The Deep Learning SDK requires CUDA Toolkit, which offers a comprehensive development environment for building new GPU-accelerated deep learning algorithms, and dramatically increasing the performance of existing applications

Scaling Up Deep Learning

Kubernetes on NVIDIA GPUs and GPU Container Runtime enables enterprises to scale up training and inference deployment to multi-cloud GPU clusters seamlessly. Developers can wrap their GPU-accelerated applications along with its dependencies into a single package and deploy with Kubernetes and deliver the best performance on NVIDIA GPUs, regardless of the deployment environment.